The present disclosure relates, generally, to systems and methods for magnetic resonance imaging (MRI) and, more particularly, to systems and methods for assessing tissue density, such as breast tissue density, using MRI.
Early detection of breast cancer with mammography has been shown in multiple randomized controlled trials to decrease mortality from breast cancer. Unfortunately, mammography has limitations and cannot detect all cancers, with approximately 20% of breast cancers occult on mammography. High mammographic breast density has been shown to increase both breast cancer risk and the difficulty in interpreting mammograms. The American College of Radiology (ACR) Breast Imaging and Reporting Data System (BI-RADS) Atlas describes four categories for breast density that are, by nature, subjective: (a) almost entirely fatty; (b) scattered areas of fibroglandular tissue (FGT) density; (c) heterogeneously dense, which may obscure small masses; and (d) extremely dense, which lowers the sensitivity of mammography to detect cancer. Distribution of women by density category is approximately: 10% fatty; 40% scattered fibroglandular; 40% heterogeneously dense; and 10% extremely dense, with 50% of women falling into the two highest categories of breast density as reported by interpreting radiologists in the United States Breast Cancer Surveillance Consortium. The reduction in mammographic sensitivity is approximately 7% for women with heterogeneously dense breasts and 13% for women with extremely dense breasts when compared with women of average breast density. It is well known that the lifetime risk in those patients with mammographically dense breasts is increased compared to those with less dense breasts. While the exact mechanism is not well understood, there is a clear need to risk-stratify patients based on breast density. Unfortunately, using mammography, there is both intra- and inter-observation variation in visually estimated breast density between two adjacent density categories. This is particularly pronounced between the two intermediate categories (b and c), which define whether or not a patient has “dense” breasts. Further, the difference in sensitivity between the least dense breast in a higher density category and the most dense breast in a lower-density category may be insignificant, limiting the clinical usefulness of breast density categorization and highlighting the need for a robust, quantitative measurement of breast density, as a predictor of future development of breast cancer.
Patients with mammographically dense breasts have an increased risk of breast cancer, with women with the highest breast density having an estimated 4- to 6-fold increased risk of breast cancer compared to women with the least dense breasts. The Food and Drug Administration (FDA)'s Mammography Quality and Standards Act (MQSA) requires facilities performing mammography to report a patient's breast density to the referring clinician in the final written report. Patient advocacy groups, such as “Are You Dense?” in the United States, have been influential in getting breast density notification laws enacted, currently active in 24 states. Most state density laws require that patients be informed if they have dense breast tissue and that dense breasts are associated with increased cancer risk. All require a statement indicating that mammography may be more limited in dense breasts.
Mammography quantifies breast density by differentiating between radiographically opaque fibroglandular tissue (stromal and epithelial tissues) and radiographically lucent fat. Several commercially available software packages provide a quantitative measurement of percentage of fibroglandular breast tissue (percentage mammographic density) calculated from the total breast area within a mammogram. These include Cumulus (University of Toronto), Quantra (Hologic Inc., Bedford, Mass.), and Volpara (Matakina). However, there are multiple limitations of mammographic-based methods of measuring breast density, including the impact of compression and breast orientation, using two-dimensional projected area (rather than volume) of tissue, variations in mammographic acquisition parameters, and the use of ionizing radiation. These limitations have limited the clinical use of mammographic breast density quantification software, and have led to interest in measuring breast tissue composition with other modalities.
Magnetic Resonance Imaging (MRI) is a widely available and accessible technology that does not use ionizing radiation. When a substance, such as human tissue, is subjected to a sufficiently large, uniform magnetic field (polarizing field B0), the individual magnetic moments of the nuclei in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) that is in the x-y plane and that is near the Larmor frequency, the net aligned moment, MZ′ may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment Mxy. A signal is emitted by the excited nuclei or “spins”, after the excitation signal B1 is terminated, and this signal may be received and processed to form an image.
When utilizing these “MR” signals to produce images, magnetic field gradients (Gx, Gy, Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. The resulting set of received MR signals are digitized and processed to reconstruct the image using one of many well known reconstruction techniques.
To do so, the signals are often weighted in different ways to give preference to or consider different sub-signals or so-called contrast mechanisms. Two basic “contrast mechanisms” commonly utilized in MR imaging are the spin-lattice (or longitudinal or T1) relaxation time or spin-spin (or transverse or T2) relaxation time. However, there are a variety of other mechanisms for eliciting contrast in MRI, including R2*. Specifically, T2* is a quantity related to T2, but including dephasing effects. That is, T2* is a quantity related to spin-spin relaxation and, in addition, relating magnetic field inhomogeneities and susceptibility effects. Often, instead of T2*, these quantities are preferably expressed in terms of relaxation, or the inverse of the T2* time constant, represented as R2*. Thus, MRI provides a variety of mechanism that can be used to analyze tissue and is gaining popularity for breast imaging.
MRI has gained popularity for breast imaging for a variety of reasons. For example, breast MRI is the most sensitive modality for the detection of breast cancer, detecting more cancers than mammography, ultrasound, or the physical breast exam. Furthermore, MRI's ability to provide 3D volumetric images that encompass the entire breast means that clinicians can better image breast tissue near the chest wall, without compression. Further still, the different contrast available from MRI allows images to be created that are weighted toward particular tissue types of interest.
Potential benefits of MRI quantification of fibroglandular tissue are multifold. MRI-derived quantification of fibroglandular breast tissue relies on the different relaxation properties of fibroglandular tissue and fat, in contrast to percentage mammographic density derived from X-ray beam attenuation in fibroglandular tissue. Thus, it is likely to have less measurement error than the subjective measurement of breast density from two-dimensional mammography in compression. MRI can be used to measure breast tissue composition in cross-section and MRI can provide three-dimensional, volumetric images without breast compression.
Regardless of imaging modality, valid quantitative imaging biomarkers must satisfy several important metrics of performance including accuracy, precision, robustness, and reproducibility. Any new biomarker must have good technical accuracy for quantifying a relevant tissue property of tissue (e.g. density of fibroglandular tissue). It must also have low variability to repeated measurements, i.e. repeatability, and it must be robust to differences in acquisition parameters such as TR, TE, flip angle, and the like. Further, the method should be reproducible across sites, vendors, and magnetic field strength. A new biomarker that satisfies these requirements and quantifies a fundamental tissue property of clinical relevance can be a valuable biomarker.
Multiple MR-based methods have been described to calculate percentage of fibroglandular tissue. These methods are typically reliant on T1-weighted pulse sequences and include threshold-based segmentation of signal intensities and clustering algorithms such as the fuzzy C-means algorithm. Chemical-shift-encoded MRI (CSE-MRI) “Dixon” fat-water separation techniques have been described, which collect data with at least two different TEs allowing separation of water and fat and result in water-only and fat-only images. More recently, some have compared a fuzzy C-means and a 3-point Dixon segmentation method for calculation of percentage fibroglandular tissue, finding that Dixon measurements were on average 10-20% higher. Thus, variability in percentage of fibroglandular tissue has been reported between methods, segmentation techniques (including inclusion or exclusion of the skin), positioning, and pulse sequences differences (including the use of chemical fat-suppression). Thus, the currently proposed methods do not satisfy the requirements for a valid biomarker as described above.
Therefore, it would be desirable to have a system and method for breast imaging that is capable of providing accurate breast density information and valid quantitative imaging biomarkers.